In the context of the options framework [1]}, several methods have been proposed for option and/or sub-goal discovery [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
However, these algorithms are not easily extendable to the case with nonlinear function approximation as needed in our case.
Alternatively, other methods using nonlinear function approximation propose to maximize coverage (diversity) of learned skills by maximizing the mutual information between options and terminal states achieved by their execution [9]}, [10]}.
It remains difficult to precisely evaluate the level of semantically independent modes of behavior that options discovered in this way afford.
In contrast, here we explicitly optimize and evaluate the sub-routines with regards to their modularity in an imitation learning setting.
